Description

Responsibilities include:

Lead the optimization of enterprise AI platforms to improve performance, reliability, scalability, and cost efficiency.

Design and manage AI platform architecture, including model routing, LLM gateways, and consumption controls.

Implement and enhance observability, monitoring, and performance analytics across AI services.

Develop strategies for token metering, rate limiting, quotas, catching, and batching to maximize platform efficiency.

Partner with engineering and business teams to establish AI platform governance, standards, and best practices.

Drive MLOps/LLMOps initiatives, ensuring seamless deployment, evaluation, and lifecycle management of AI models.

Required Qualifications:

Bachelor’s degree in computer science, Engineering, Information Technology, or a related field.

8-10 years of experience in AI platform engineering, enterprise architecture, or cloud-based technology environments.

Strong experience with AI/ML platforms, including LLM gateways, model routing, token management, observability, and platform optimization.

Expertise in MLOps/LLMOps practices, platform governance, scalability, reliability engineering, and cost optimization strategies

Key Skills

Education

Any Graduate